hw7 - Lien Veldboom

import altair as alt
import pandas as pd

gas = pd.read_csv('https://calvin-data304.netlify.app/data/pump_price_for_gasoline_us_per_liter.csv')
countries = pd.read_csv('https://raw.githubusercontent.com/lukes/ISO-3166-Countries-with-Regional-Codes/master/all/all.csv')
gas_long = gas.melt(id_vars='country')
gas_na = gas_long.dropna(subset=['value'])
merged = pd.merge(gas_na, countries[['name', 'alpha-3', 'region']], how='left',
                  left_on='country', right_on='name')
merged_2 = merged.dropna(subset=['region'])
merged_final = merged_2.astype({'value': 'float'})
merged_final['Price in Gallon'] = merged_final['value']*3.78541

chart = alt.Chart(merged_final).mark_bar().encode(
    x=alt.X('region:N', title='Region',sort='-y'),
    y=alt.Y('mean(value):Q', title='Mean Gas Price per Gallon'),
    color=alt.Color('region:N', title='Region')
).properties(
    width=600,
    title='European Countriess have Highest Mean Price per Gallon on Gas')
chart

democracy = pd.read_csv('https://calvin-data304.netlify.app/data/wvs.csv')

alt.data_transformers.disable_max_rows()

alt.Chart(democracy).mark_bar().encode(
    y=alt.Y('country:O', sort='-x'),
    x='count():Q',
    color ='country')

worldage3 = democracy.groupby(['country', 'age3'])['age'].agg(['min', 'max']).reset_index()

alt.Chart(worldage3).mark_bar().encode(
    x=alt.X('country:N', title='Country'),
    y=alt.Y('min:Q', title='Age'),
    y2='max:Q',
    color=alt.Color('age3:N', title="Age Group"))

From this graph, you can see that the 3 different age groups are - 1 = 0-29, but based on this graph I would say it is more 17-29 with no participants younger than 15.

2= 30-49

3= 50+

It seems it is the ame for each country as well.

worldage6 = democracy.groupby(['country', 'age6'])['age'].agg(['min', 'max']).reset_index()

alt.Chart(worldage6).mark_bar().encode(
    x=alt.X('country:N', title='Country'),
    y=alt.Y('min:Q', title='Age'),
    y2='max:Q',
    color=alt.Color('age6:N', title="Age Group"))

From this graph, you can see that the 6 different age groups are - 1 = 0-24, but based on this graph I would say it is more 17-24 with no participants younger than 15.

2= 25-34

3= 35-44

4= 45-54

5= 55-64

6 = 65+

It seems it is the same for each country as well.

worldproportion = democracy[['country', 'age6', 'democracy_importance']]

worldproportion['responses_10'] = worldproportion.loc[:,'democracy_importance'].apply(lambda x: 1 if x == 10 else 0)

base = alt.Chart(worldproportion).encode(
    x=alt.X("age6:O", sort='-x',title=""),
    y=alt.Y("responses_10:Q", title=""))

error = base.mark_errorband(extent="ci")

point = base.mark_circle(size=20).encode(
    y=alt.Y("mean(responses_10):Q"))

line = base.mark_line().encode(
    y=alt.Y("mean(responses_10):Q"))

final_proportion = error + line + point

final_proportion.facet(
    column = alt.Column("country:N", title="")).resolve_scale(y='independent').properties(
    title="Younger Generations Less Likley to say it is Essential to Live in a Democracy"
)

/tmp/ipykernel_1741/877240211.py:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

average_democracy = democracy[['country', 'age6', 'democracy_importance']]

base = alt.Chart(average_democracy).encode(
    x=alt.X("age6:O", sort='-x',title=""),
    y=alt.Y("democracy_importance:Q", title=""))

error = base.mark_errorband(extent="ci")

point = base.mark_circle(size=15).encode(
    y=alt.Y("mean(democracy_importance):Q"))
    
line = base.mark_line().encode(
    y=alt.Y("mean(democracy_importance):Q"))

average_final = error + line + point

average_final.facet(
    column = alt.Column("country:N", title="")).resolve_scale(y='independent').properties(
    title="On Average People who say it is Essential to Live in a Democracy Decreases the Younger they are ")

worldproportion = democracy[['country', 'age', 'democracy_importance']]

worldproportion['responses_10'] = worldproportion.loc[:,'democracy_importance'].apply(lambda x: 1 if x == 10 else 0)

base = alt.Chart(worldproportion).encode(
    x=alt.X("age:O", sort='-x',title="",axis=alt.Axis(values=[0,10,20,30,40,50,60,70,80,90,100])),
    y=alt.Y("responses_10:Q", title="")).properties(
    width=500,
    height=300)
    
    
error = base.mark_errorband(extent="ci")

point = base.mark_circle(size=20).encode(
    y=alt.Y("mean(responses_10):Q"))

line = base.mark_line().encode(
    y=alt.Y("mean(responses_10):Q"))

final_proportion = error + line + point

final_proportion.facet(
    column = alt.Column("country:N", title="")).resolve_scale(y='independent').properties(
    title="Younger Generations Less Likley to say it is Essential to Live in a Democracy"
)

/tmp/ipykernel_1741/2058397284.py:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

average_democracy = democracy[['country', 'age', 'democracy_importance']]

base = alt.Chart(average_democracy).encode(
    x=alt.X("age:O", sort='-x',title="",axis=alt.Axis(values=[0,10,20,30,40,50,60,70,80,90,100])),
    y=alt.Y("democracy_importance:Q", title="")).properties(
    width=500,
    height=300)
    
error = base.mark_errorband(extent="ci")

point = base.mark_circle(size=15).encode(
    y=alt.Y("mean(democracy_importance):Q"))
    
line = base.mark_line().encode(
    y=alt.Y("mean(democracy_importance):Q"))

average_final = error + line + point

average_final.facet(
    column = alt.Column("country:N", title="")).resolve_scale(y='independent').properties(
    title="On Average People who say it is Essential to Live in a Democracy Decreases the Younger they are ")

I do not like either of these graphs more using age instead of age6. It is really hard to look at and too busy to see any real patterns within the data. Specifally for the proportion graph, when you have an age on the graph that doens’t have an anwer the graph goes to 0. This makes it look like it jumps up and down so much, when in reality it might not. The confidence interval is also so much wider for the age graph vs the aget graph.

loess= democracy[['age', 'country', 'democracy_importance']]

loess = loess.groupby(['age', 'country'])['democracy_importance'].mean().reset_index()

point = alt.Chart(loess ).mark_point().encode(
    x=alt.X("age:O", sort='-x',title="",axis=alt.Axis(values=[0,10,20,30,40,50,60,70,80,90,100])),
    y='democracy_importance:Q').properties(
    width=500,
    height=300)

line = alt.Chart(loess).transform_loess(
    'age', 'democracy_importance', groupby=['country']
).mark_line(
).encode(
    x=alt.X("age:O", sort='-x',title="",axis=alt.Axis(values=[0,10,20,30,40,50,60,70,80,90,100])),
    y='democracy_importance:Q').properties(
    width=500,
    height=300)

loess_final = line + point

loess_final.facet(
    column = alt.Column("country:N", title="")).resolve_scale(y='independent').properties(
    title="LOcally Estimated Scatterplot Smoothing  ")

I like this graph more. You can see the overall pattern way better than what you could in the other graph that used ages instead of age6. It isn’t overwheling. While you can see the overall trend, with the dots you can also see the actual mean of response from each age too.

regression= democracy[['age', 'country', 'democracy_importance']]

regression = regression.groupby(['age', 'country'])['democracy_importance'].mean().reset_index()

point = alt.Chart(regression ).mark_point().encode(
     x=alt.X("age:O", sort='-x',title="",axis=alt.Axis(values=[0,10,20,30,40,50,60,70,80,90,100])),
    y='democracy_importance:Q').properties(
    width=500,
    height=300)

line = alt.Chart(regression).transform_regression(
    'age', 'democracy_importance', groupby=['country'], method='linear'
).mark_line(
).encode(
     x=alt.X("age:O", sort='-x',title="",axis=alt.Axis(values=[0,10,20,30,40,50,60,70,80,90,100])),
    y='democracy_importance:Q').properties(
    width=500,
    height=300)

linear_final = line + point

linear_final.facet(
    column = alt.Column("country:N", title="")).resolve_scale(y='independent').properties(
    title="Linear Regression")

regression= democracy[['age', 'country', 'democracy_importance']]

regression = regression.groupby(['age', 'country'])['democracy_importance'].mean().reset_index()

point = alt.Chart(regression ).mark_point().encode(
    x=alt.X("age:O", sort='-x',title="",axis=alt.Axis(values=[0,10,20,30,40,50,60,70,80,90,100])),
    y='democracy_importance:Q').properties(
    width=500,
    height=300)

line = alt.Chart(regression).transform_regression(
    'age', 'democracy_importance', groupby=['country'], method='poly'
).mark_line(
).encode(
     x=alt.X("age:O", sort='-x',title="",axis=alt.Axis(values=[0,10,20,30,40,50,60,70,80,90,100])),
    y='democracy_importance:Q').properties(
    width=500,
    height=300)

poly_final = line + point

poly_final.facet(
    column = alt.Column("country:N", title="")).resolve_scale(y='independent').properties(
    title="Polynomial Regression")